Motivation: Barrow Alcohol Study

Loading...

From the course by University of Washington

Communicating Data Science Results

121 оценки

University of Washington

Communicating Data Science Results

121 оценки

Course 3 of 4 in the Specialization Data Science at Scale

Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit. While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so. Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations. Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both. Learning Goals: After completing this course, you will be able to: 1. Design and critique visualizations 2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science 3. Use cloud computing to analyze large datasets in a reproducible way.

From the lesson

Privacy and Ethics

Big Data has become closely linked to issues of privacy and ethics: As the limits on what we *can* do with data continue to evaporate, the question of what we *should* do with data becomes paramount. Motivated in the context of case studies, you will learn the core principles of codes of conduct for data science and statistical analysis. You will learn the limits of current theory on protecting privacy while still permitting useful statistical analysis.

Motivation: Barrow Alcohol Study5:57

Barrow Study Problems4:59

Reifying Ethics: Codes of Conduct6:50

ASA Code of Conduct: Responsibilities to Stakeholders4:43

Other Codes of Conduct6:51

Examples of Codified Rules: HIPAA3:30

Meet the Instructors

Bill Howe
Director of Research
Scalable Data Analytics

[MUSIC]

In this module, we're gonna talk about ethics and privacy and

as with many of the topics in these courses in this specialization,

this topic warrants a entire course unto itself.

But, just like we've done with the other topics, what we're going to do is try to

give you an overview of the important concepts in the area.

We're going to motivate this with real world examples and we're going to do

a technical deep dive into particularly important concepts, or to make sure you

have an intuition for how to use them and how to apply them elsewhere.

What we hope is that this will be enough of an appetite whetting exercise

to encourage you to go take an entire course on ethics and

privacy in big data and data science, of which there are many starting to emerge.

We've done this with databases and large scale data management systems.

We've done this with machine learning.

We've done this with statistics.

We've done this with visualization.

Now, we're gonna try to give it a treatment of this topic.

Cherry picking topics that we think are especially relevant

given the theme of the course and given things that I'm interested in.

I want to start off with a study that was conducted quite a while ago in 1979,

in Barrow, Alaska.

Barrow is the northernmost point in the United States,

northernmost city in the United States and it's a rural community.

This is a zoom in shot and

you can see those lakes in the aerial, you can see those lakes, as well.

This was a Native American community that has been hunting whales for

thousands of years and since World War II,

there have been significant economic changes over a short time.

Native leaders and city officials were concerned about the use of alcohol

in the community and the associated violence and invited a group of sociology

researchers to assess the problem and work with them to devise solutions.

So, a team came in, and the methods used here were to collect a representative

sample of everyone over the age of 15.

Although, as we'll mention in a moment, they targeted

the Native community in particular, and they interviewed them on attitudes and

values associated with the use of alcohol, including psychological histories.

They gave them a standardized alcoholism screening test that had been devised

previously and asked them to draw a picture of a person which was intended

to determine cultural identity.

If you draw someone in a certain style,

than that suggests that you're coming from the perspective of a particular community.

If you draw someone in another style,

it shows you're from another particular community and

that's a method that was accepted at the time.

The results of the study, the particular numbers are not terribly important,

you can look them up but

the overall effect was that alcoholism was pretty extreme there.

There was widespread use and

abuse of alcohol and associated with the problems that that would entail.

The reason why I want to bring up this study that was not so much just for

the results itself, it's the manner in which it was released and

the overall context in which this study was done

in order to motivate some of the ethical considerations.

In particular, the results of this study were announced unilaterally and

publicly in two forms simultaneously.

One was a report was released and simultaneously,

a press conference was held to the Barrow community,

crucially, without representation from the tribal communities that

were reflected in the study, even at the press conference.

The report was picked up by the press and it became a front page New York Times

story titled alcohol plagues Eskimos, alcoholism plagues Eskimo village.

You can imagine the effect here, the quotes here in this article you can see,

they're practically committing suicide by mass alcoholism.

University of Pennsylvania researchers said yesterday, the alcoholism rate is 72%

among the 2,000 Eskimo men and women in the village of Barrow.

Violence is becoming the leading cause of injury and death.

So the effects of this study on the populous were pretty immediate.

If you were a member of the Barrow community, if you're a member of

the Iñupiat tribe, you were associated with the brand

presented in this article in a very, very public way.

Further, the results of the Barrow Alcohol Study, it says here were revealed in

the context of a press conference that was held far away from the native village.

Without the presence and much less the knowledge or

consent of any community member who might have been able to present any context

concerning the socioeconomic conditions of the village.

So the study results suggested that nearly all adults of the Iñupiat were alcoholics.

In addition to the shame, the town's Standard & Poor bond rating suffered as

a result, which in turn decreased the tribe's ability to secure funding for

much needed projects.

This was a direct financial harm incurred by the community,

the inability to get loans in order to proceed with public works projects.

There's a number of articles that were written after this,

reflecting how they were upset about this and the harms they had faced.

There's this subtle side effect too that's one of the reasons why this study is so

well known in the medical and

sociological and ethics' communities, as an example,

is that research itself has a bad reputation among Native peoples.

Not only in this area but widespread, in part, because of this study and

others like it.

Research has been described as probably one of the dirtiest words in

the indigenous world's vocabulary as a result of this.

This is important to understand what went wrong here and

there's a couple of particular points that I wanna make about this particular study.

>> [MUSIC]

Ознакомьтесь с нашим каталогом

Присоединяйтесь бесплатно и получайте персонализированные рекомендации, обновления и предложения.

Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.

© Coursera Inc., 2018 Все права защищены.

Загрузить из App Store

Загрузить в Google Play